 Artificial intelligence, AI, has made significant advancements in recent years, allowing for automated technologies that can eliminate or reduce human error in analyzing health data. Despite this, pathologists still need to manually analyze histopathologic tissues under the microscope, which is not only time consuming but also prone to human error. Automating this process with machine learning, ML, algorithms could improve monitoring strategies, as well as provide the ability to identify the preliminary stages of cancer or other diseases. Additionally, the increased opportunity to forecast and take control of the spread of global diseases could help create a preliminary analysis and viable solutions. Our study aimed to expand the ability to find more accurate ML methods and techniques that can lead to detecting tumor damage tissues and histopathological whole slide images, WSI's. We tested various convolutional models and found that they worked well on groups of different sizes when properly trained. With the test time augmentation, TTA, method, the result improved to 0.96870, and with the addition of the multi-model ensemble, it improved to zero. This article was authored by Mantis Cundrodis, Edita Mizonin, and Dimitri Sesek. We are article.tv, links in the description below.